Ghostbuster: How to Spot Fake Texts Written by AI

In an era where the lines between human and machine-generated content blur, Ghostbuster: How to Spot Fake Texts Written by AI emerges as a beacon, illuminating the pathways to discerning the subtle fingerprints of artificial intelligence. As we embark on a journey through the intricate web of algorithms and linguistic patterns, this book serves as a guide for the curious, the vigilant, and the discerning.

Introduction

Have you ever pondered whether a human or a machine authored the text you’re currently reading online? Additionally, With the rapid advances in natural language generation (NLG) technologies, it is becoming harder and harder to tell the difference. NLG models, such as GPT-3, can produce fluent and coherent texts on various topics and styles, often fooling human readers and evaluators. However, these models also pose serious ethical and social challenges, such as spreading misinformation, plagiarism, and manipulation.

In this blog post, I will introduce you to Ghostbuster, a state-of-the-art system for detecting text ghostwritten by large language models, developed by researchers at the University of California, Berkeley. Ghostbuster is a novel and powerful tool that can help you identify and verify the authenticity and origin of the texts you encounter online. I will explain the main features and advantages of Ghostbuster, and show you some examples of how it works in practice.

Join TechoVedas Community here

Feature 1: Model-agnostic Detection

One of the key features of Ghostbuster is that it does not require access to token probabilities from the target model, making it useful for detecting text generated by black-box models or unknown model versions. This means that Ghostbuster can detect text generated by any NLG model, regardless of its architecture, parameters, or training data.

This represents a substantial improvement over prior methods that frequently depended on particular model features or assumptions, making them susceptible to bypassing by merely altering the model or its settings.

For example, suppose you want to detect text generated by ChatGPT, a commercial NLG model that powers several chatbots and conversational agents online. ChatGPT is a black-box model, meaning that you do not have access to its internal workings or outputs. How can you tell if the text you are chatting with is written by ChatGPT or a human? Ghostbuster can help you with that, by analyzing the text and comparing it with a reference corpus of human-written texts. Ghostbuster can detect subtle differences and patterns that distinguish ChatGPT-generated texts from human-written texts, such as word choices, sentence structures, and semantic coherence.

Read More: Is Apple Preparing for Foldable Phones? – techovedas

Feature 2: Cross-domain Generalization

Another key feature of Ghostbuster is that it achieves high performance across different writing domains, such as student essays, creative writing, and news articles. This means that Ghostbuster can detect text generated by any NLG model, regardless of its domain, genre, or style. This represents a substantial improvement over prior methods, which frequently exhibited domain-specific biases or limitations, and one could easily deceive them by altering the domain or the text style.

For example, suppose you want to detect text generated by GPT-3, which can produce texts on various domains and styles, such as fiction, poetry, and journalism. How can you tell if the text you are reading is written by GPT-3 or a human? Ghostbuster can help you with that, by analyzing the text and comparing it with a reference corpus of human-written texts from the same domain and style. Ghostbuster can detect subtle and nuanced differences and patterns that distinguish GPT-3-generated texts from human-written texts, such as tone, mood, and creativity.

To demonstrate Ghostbuster’s cross-domain generalization ability, the researchers released three new datasets of human-written and AI-generated texts from different domains: student essays, creative writing, and news articles.

Ghostbuster achieved high accuracy and recall on all three datasets, outperforming previous methods and human baselines. The datasets are publicly available for anyone who wants to test their own methods or skills on detecting AI-generated texts.

Read More: 7 Tech Companies Which gained a total of $4.96T in market cap in 2023 – techovedas

Feature 3: Robustness to Perturbations and Paraphrasing

Ghostbuster exhibits robustness to a range of perturbations and paraphrasing attacks, including spelling errors, word substitutions, and sentence reordering. This capability allows Ghostbuster to identify text generated by any NLG model, irrespective of the modifications or alterations applied.

“This marks a substantial advancement compared to earlier methods, which frequently depended on specific text features or patterns and were susceptible to being deceived through text alterations or obfuscation.”

To demonstrate Ghostbuster’s robustness to perturbations and paraphrasing, the researchers conducted several experiments where they applied different types of modifications to the AI-generated texts, such as adding spelling errors, replacing words with synonyms, and shuffling sentences.

Ghostbuster exhibits robustness against a range of perturbations and paraphrasing attacks, including spelling errors, word substitutions, and sentence reordering. It can effectively identify text generated by any NLG model, irrespective of the modifications or alterations made. This represents a substantial advancement over prior methods that depended on specific text features or patterns, making them susceptible to evasion through changes or obfuscation in the text.

Conclusion

In this blog post, I have introduced you to Ghostbuster, a state-of-the-art system for detecting text ghostwritten by large language models. I have explained the main features and advantages of Ghostbuster, and showed you some examples of how it works in practice. I hope you have learned something new and useful about how to spot fake texts written by AI, and why it matters.

Ghostbuster is not a perfect or a final solution. It is a work in progress, and it has its own limitations and challenges. For instance, Ghostbuster requires a large and divers8e reference corpus of human-written texts, which may not always be available or accessible. Ghostbuster also relies on statistical and linguistic features, which may not capture all the aspects and dimensions of text quality and credibility.

Therefore, Ghostbuster is not a substitute for human judgment or evaluation. It is a complement and a support. It is a tool that can help us detect and flag AI-generated texts, but it is up to us to verify and validate them.

What do you think of Ghostbuster? Have you ever encountered or used AI-generated texts? How do you tell if a text is written by a human or a machine? Share your thoughts and experiences in the comments below. And remember: don’t believe everything you read online. Be a ghostbuster.

Editorial Team
Editorial Team
Articles: 1901